Anti-Bias Training Across the Data-Life Cycle: Project Data Inclusion


Despite a growing awareness of the lack of representation for women and communities of color in large-scale data sets, no standard educational approach to address discriminatory bias exists across data science fields. This team aims to design and offer community-based, anti-bias training in order to raise awareness of these inequities. In addition, they will address implicit and explicit racial and intersectional bias during data collection by first focusing on public health information. This team hopes that its work will address the lack of inclusion and equity in the data life cycle, and through anti-bias training interventions, it will target students and professionals across disciplines. Ultimately, these students can become ambassadors to educate and engage in anti-bias efforts in their future professional contexts.


Jennifer Kahn, Teaching and Learning; Soyeon Ahn, Education and Psychological Studies, Debbiesiu Lee, Education and Psychological Studies, Ching-Hua Chuan, Cinema and Interactive Media; Patricia Jones, Medicine